Octopus: Embodied Vision-Language Programmer from Environmental Feedback

• URL: http://arxiv.org/abs/2310.08588v2
• Date: Sun, 20 Oct 2024 17:57:03 GMT
• Title: Octopus: Embodied Vision-Language Programmer from Environmental Feedback
• Authors: Jingkang Yang, Yuhao Dong, Shuai Liu, Bo Li, Ziyue Wang, Chencheng Jiang, Haoran Tan, Jiamu Kang, Yuanhan Zhang, Kaiyang Zhou, Ziwei Liu,
• Abstract summary: Embodied vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation. Octopus is designed to 1) proficiently comprehend an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code.
• Score: 58.04529328728999
• License:
• Abstract: Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works either output detailed action sequences at the manipulation level or only provide plans at an abstract level, leaving a gap between high-level planning and real-world manipulation. To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation. Octopus is designed to 1) proficiently comprehend an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code. To facilitate Octopus model development, we introduce OctoVerse: a suite of environments tailored for benchmarking vision-based code generators on a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games such as Grand Theft Auto (GTA) and Minecraft. To train Octopus, we leverage GPT-4 to control an explorative agent that generates training data, i.e., action blueprints and corresponding executable code. We also collect feedback that enables an enhanced training scheme called Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we demonstrate Octopus's functionality and present compelling results, showing that the proposed RLEF refines the agent's decision-making. By open-sourcing our simulation environments, dataset, and model architecture, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community.

Related papers

• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• LEGENT: Open Platform for Embodied Agents [60.71847900126832]
  We introduce LEGENT, an open, scalable platform for developing embodied agents using Large Language Models (LLMs) and Large Multimodal Models (LMMs) LEGENT offers a rich, interactive 3D environment with communicable and actionable agents, paired with a user-friendly interface. In experiments, an embryonic vision-language-action model trained on LEGENT-generated data surpasses GPT-4V in embodied tasks.
  arXiv  Detail & Related papers  (2024-04-28T16:50:12Z)
• MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments [82.67236400004826]
  We introduce the Multimodal Embodied Interactive Agent (MEIA), capable of translating high-level tasks expressed in natural language into a sequence of executable actions. MEM module enables MEIA to generate executable action plans based on diverse requirements and the robot's capabilities.
  arXiv  Detail & Related papers  (2024-02-01T02:43:20Z)
• Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds [37.22688246779871]
  Large language models (LLMs) can equip embodied agents with the self-driven capability to interact with the world. LLMs tend to overlook the visual richness of open worlds, rendering the entire interactive process akin to "a blindfolded text-based game" We propose Steve-Eye, an end-to-end trained large multimodal model designed to address this limitation.
  arXiv  Detail & Related papers  (2023-10-20T03:22:05Z)
• Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments [5.217870815854702]
  We propose using program synthesis to imitate reinforcement learning policies. We adapt the state-of-the-art program synthesis system DreamCoder for learning concepts in grid-based environments.
  arXiv  Detail & Related papers  (2023-09-07T11:46:57Z)
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models [38.503337052122234]
  Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation. We aim to synthesize robot trajectories for a variety of manipulation tasks given an open-set of instructions and an open-set of objects. We demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions.
  arXiv  Detail & Related papers  (2023-07-12T07:40:48Z)
• Embodied Task Planning with Large Language Models [86.63533340293361]
  We propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning with physical scene constraint. During inference, we discover the objects in the scene by extending open-vocabulary object detectors to multi-view RGB images collected in different achievable locations. Experimental results show that the generated plan from our TaPA framework can achieve higher success rate than LLaVA and GPT-3.5 by a sizable margin.
  arXiv  Detail & Related papers  (2023-07-04T17:58:25Z)
• AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation [50.737355245505334]
  We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks. The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation.
  arXiv  Detail & Related papers  (2023-05-30T09:54:20Z)
• Chat with the Environment: Interactive Multimodal Perception Using Large Language Models [19.623070762485494]
  Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. Our study demonstrates that LLMs can provide high-level planning and reasoning skills and control interactive robot behavior in a multimodal environment.
  arXiv  Detail & Related papers  (2023-03-14T23:01:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.